DISSERTATION RESEARCH: Lizards of the Family Teiidae: phylogeny, historical biogeography, and continental-scale species delimitation

亚马逊的racerunner（Ameiva ameiva）分布在四个非常不同的南美洲生物群落中，覆盖面积大于美国大陆。 该项目将对这种蜥蜴物种进行第一次分布范围内的地理研究，为发现该物种的神秘多样性提供机会（即，新物种），以及一个独特的机会，以评估在热带雨林和干燥的栖息地多样化的竞争假说。 为亚马逊赛跑者收集的数据也将用于解决气候变化对物种的未来风险。 这项工作是与南美同事合作研究关键的生理特征（例如，温度偏好和性能），以重建物种分布模型和评估灭绝风险。 由于模拟历史分布是基于古气候数据，这些相同的模型可以用来预测温度升高对未来生物分布的影响。 本研究的结果将被开发成一个教学模块，用于向非生物专业的学生展示假设检验中使用的真实世界观察结果，这里侧重于物种形成和地理学。 从研究人员广泛的实地和实验室工作中获得的真实的例子将为学生提供个性化的数据，并提高他们对科学的多方面性质及其结论的性质的理解。初步结果表明，阿梅瓦阿梅瓦代表了一个复杂的物种，将通过尽可能多的分布抽样进行测试，并产生桑格，NextGen，和几类形态学数据来定义物种边界。 研究人员将访问巴西的三个爬虫收藏馆，测量博物馆标本的形态数据。 除形态学数据收集外，还对200个A. ameiva将被纳入新开发的物种划界（SDL）方法。 最近开发的SDL贝叶斯方法，整合形态与分子数据（iBPP）表现良好，在各种情况下，这将是本研究中使用的首选（但不是唯一的）方法。 多样化的假设亚马逊和干对角线南美洲将使用SNP数据与程序，允许分析历史情景和总结的方法来估计每个假设的强度进行比较。

The Amazon racerunner (Ameiva ameiva) is distributed across four very different South American biomes that cover an area larger than the continental US. This project will perform the first distribution-wide, phylogeographic study of this lizard species, providing opportunities to find cryptic diversity in the group (i.e., new species) as well as a unique opportunity to evaluate competing hypotheses of diversification in both rainforests and dry habitats. Data collected for the Amazon racerunner will also be used to address future risk to species due to climate change. The work is a collaboration with South American colleagues to study key physiological traits (e.g., temperature preference and performance) to reconstruct species distribution models and assess extinction risk. Because modeling historical distributions is based on paleoclimate data, these same models can be used to predict the effect that increasing temperatures will have on future distributions of organisms. Results from this research will be developed into a teaching module and used to show non-Biology major students real-world observations used in hypothesis testing, here focused on speciation and biogeography. Real examples from the researchers' extensive field and lab work will personalize the data for students and enhance their appreciation of the multi-faceted nature of science, and the nature of its conclusions.Preliminary results suggests that Ameiva ameiva represents a complex of species, which will be tested by sampling as much of the distribution as possible, and generating a combination of Sanger, NextGen, and several classes of morphological data to define species boundaries. Researchers will visit three herpetological collections in Brazil to measure morphological data from museum specimens. In addition to morphological data collection, 100s-1000s of SNPs (ddRADseq) for 200 individuals of A. ameiva will be incorporated into newly developed species delimitation (SDL) methods. A recently developed SDL Bayesian approach that integrates morphological with molecular data (iBPP) was shown to perform well under a variety of scenarios, and this will be the preferred (but not exclusive) method used in this study. Diversification hypotheses for the Amazon and Dry Diagonal of South America will be compared using SNP data with programs that allow the analysis of historical scenarios and a summary approach to estimate the strength of each hypothesis.